Multiomics Integration for Identifying Treatment Targets, Drug Development, and Diagnostic Designs in PAH
Bibliographic record
Abstract
Unraveling the complexities of pulmonary arterial hypertension (PAH) is challenging due to its multifaceted nature, encompassing molecular, cellular, tissue, and organ-level alterations. The advent of omics technologies, including genomics, epigenomics, transcriptomics, metabolomics, and proteomics, has generated a vast array of public and nonpublic datasets from both humans and model organisms, opening new avenues for understanding PAH. However, the insights provided by individual omics datasets into the molecular mechanisms of PAH are inherently limited. In response, efforts are increasing to develop integrative omics approaches designed to synthesize multidimensional omics data into a cohesive understanding of the molecular dynamics of PAH. In this review, we discuss various strategies for integrating multiomic data and illustrate their application in PAH research. We explore the challenges encountered and the profound potential of leveraging omics data for comprehensive molecular insight as well as for the identification of novel therapeutic targets and biomarkers specific to PAH. Furthermore, in this review, we seek to elucidate the process and rationale behind conducting integrative omics studies in PAH, raising critical questions about the feasibility and future prospects of multiomic integration in unraveling the complexities of this disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".